Brain#

The results for the CNeuroMod quantitative MRI longitudinal stability study for the brain are displayed below in two sections, Quantitative MRI and Diffusion, reflecting the two separate pipelines used to processes these datasets (see the Materials and Methods section for pipeline diagrams). The mean intrasubject and intersubject COVs are reported in tables below each respective figure, as well as the intrasubject COV standard deviation.

The figures are presented in an interactive format using the Plotly framework, you can hover to view the values of each datapoints, use the dropdown box (when applicable) to change between metrics, click and drag to zoom in, etc.

This page was generated using an Jupyter Notebook, and all the commands run to reproduce the figure using the prepared and packaged ROI data are shown prior to the figures. If you’d like to re-run the notebook, you can click the 🚀 icon on the top right of this page and then on “Binder” to open a MyBinder session in your browser - no installation is required.

Quantitative MRI#

Code imports#

# Python imports 
from IPython.display import clear_output
from pathlib import Path
import numpy as np

import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.precision', 1)

# Import custom tools
from tools.data import Data
from tools.plot import Plot
from tools.stats import Stats

Download data#

data_type = 'brain'
release_version = 'latest'

dataset = Data(data_type)
dataset.download(release_version)

--2023-04-12 16:28:23--  https://github.com/courtois-neuromod/anat-processing/releases/download/r20220921/neuromod-anat-brain-qmri.zip
Resolving github.com (github.com)... 192.30.255.112
Connecting to github.com (github.com)|192.30.255.112|:443... connected.
HTTP request sent, awaiting response... 

302 Found
Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/59a68bb3-4423-49ab-959d-247690acbebc?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230412%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230412T162823Z&X-Amz-Expires=300&X-Amz-Signature=813f48329642067f9051db4ba7a954bf3c5122929d9a58c559be39c66cd7a2f2&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dneuromod-anat-brain-qmri.zip&response-content-type=application%2Foctet-stream [following]
--2023-04-12 16:28:24--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/59a68bb3-4423-49ab-959d-247690acbebc?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230412%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230412T162823Z&X-Amz-Expires=300&X-Amz-Signature=813f48329642067f9051db4ba7a954bf3c5122929d9a58c559be39c66cd7a2f2&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dneuromod-anat-brain-qmri.zip&response-content-type=application%2Foctet-stream
Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...
Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 

200 OK
Length: 1301347 (1.2M) [application/octet-stream]
Saving to: ‘neuromod-anat-brain-qmri.zip’

     0K .......... .......... .......... .......... ..........  3% 25.2M 0s
    50K .......... .......... .......... .......... ..........  7% 60.2M 0s
   100K .......... .......... .......... .......... .......... 11% 25.2M 0s
   150K .......... .......... .......... .......... .......... 15%  204M 0s
   200K .......... .......... .......... .......... .......... 19% 58.3M 0s
   250K .......... .......... .......... .......... .......... 23%  277M 0s
   300K .......... .......... .......... .......... .......... 27%  226M 0s
   350K .......... .......... .......... .......... .......... 31%  191M 0s
   400K .......... .......... .......... .......... .......... 35%  211M 0s
   450K .......... .......... .......... .......... .......... 39% 75.6M 0s
   500K .......... .......... .......... .......... .......... 43% 72.0M 0s
   550K .......... .......... .......... .......... .......... 47%  196M 0s
   600K .......... .......... .......... .......... .......... 51%  128M 0s
   650K .......... .......... .......... .......... .......... 55%  220M 0s
   700K .......... .......... .......... .......... .......... 59%  295M 0s
   750K .......... .......... .......... .......... .......... 62%  227M 0s
   800K .......... .......... .......... .......... .......... 66%  280M 0s
   850K .......... .......... .......... .......... .......... 70%  128M 0s
   900K .......... .......... .......... .......... .......... 74%  294M 0s
   950K .......... .......... .......... .......... .......... 78%  286M 0s
  1000K .......... .......... .......... .......... .......... 82%  180M 0s
  1050K .......... .......... .......... .......... .......... 86%  188M 0s
  1100K .......... .......... .......... .......... .......... 90%  284M 0s
  1150K .......... .......... .......... .......... .......... 94%  257M 0s
  1200K .......... .......... .......... .......... .......... 98%  262M 0s
  1250K .......... ..........                                 100%  345M=0.01s

2023-04-12 16:28:24 (111 MB/s) - ‘neuromod-anat-brain-qmri.zip’ saved [1301347/1301347]

Load data plot it#

qMRI Metrics#

dataset.load()
fig_gm = Plot(dataset, plot_name = 'brain-1')

fig_gm.title = 'Brain qMRI microstructure measures'

# If you're running this notebook in a Jupyter Notebook (eg, on MyBinder), change 'jupyter-book' to 'notebook'
fig_gm.display('jupyter-book')

Statistics#

White Matter#
stats_wm = Stats(dataset)
stats_wm.build_df('WM')
stats_wm.build_stats_table()
display(stats_wm.stats_table)
T1 (MP2RAGE) T1 (MTsat) MTR MTsat
intrasubject COV mean [%] 0.6 2.3 0.6 1.7
intrasubject COV std [%] 0.2 0.8 0.1 0.5
intersubject mean COV [%] 1.9 3.5 0.4 2.2
Grey Matter#
stats_gm = Stats(dataset)
stats_gm.build_df('GM')
stats_gm.build_stats_table()
display(stats_gm.stats_table)
T1 (MP2RAGE) T1 (MTsat) MTR MTsat
intrasubject COV mean [%] 0.4 3.1 0.8 2.7
intrasubject COV std [%] 0.1 1.6 0.2 1.2
intersubject mean COV [%] 1.0 5.7 1.2 4.5

Diffusion#

data_type = 'brain-diffusion-cc'
release_version = 'latest'

dataset = Data(data_type)
dataset.download(release_version)

--2023-04-12 16:28:26--  https://github.com/courtois-neuromod/anat-processing/releases/download/r20230110/brain-diffusion-cc.zip
Resolving github.com (github.com)... 192.30.255.112
Connecting to github.com (github.com)|192.30.255.112|:443... connected.
HTTP request sent, awaiting response... 

302 Found
Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/6e6dd34d-c009-4079-bea8-df5eea106c89?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230412%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230412T162826Z&X-Amz-Expires=300&X-Amz-Signature=778cb347f0ba0d05001948ba7208675e2d38583efacb4045a9d2e9b2332ceffc&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dbrain-diffusion-cc.zip&response-content-type=application%2Foctet-stream [following]
--2023-04-12 16:28:26--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/6e6dd34d-c009-4079-bea8-df5eea106c89?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230412%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230412T162826Z&X-Amz-Expires=300&X-Amz-Signature=778cb347f0ba0d05001948ba7208675e2d38583efacb4045a9d2e9b2332ceffc&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dbrain-diffusion-cc.zip&response-content-type=application%2Foctet-stream
Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.109.133, ...
Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.111.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 15248 (15K) [application/octet-stream]
Saving to: ‘brain-diffusion-cc.zip’

     0K .......... ....                                       100% 8.17M=0.002s

2023-04-12 16:28:27 (8.17 MB/s) - ‘brain-diffusion-cc.zip’ saved [15248/15248]

dataset.load()

fig_diff = Plot(dataset, plot_name = 'brain-diff-cc')

fig_diff.title = 'Brain diffusion measures (corpus callosum)'

# If you're running this notebook in a Jupyter Notebook (eg, on MyBinder), change 'jupyter-book' to 'notebook'
fig_diff.display('jupyter-book')

Statistics#

Genu#

stats_cc1 = Stats(dataset)
stats_cc1.build_df('genu')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
FA (DWI) MD (DWI) RD (DWI)
intrasubject COV mean [%] 0.8 1.0 1.3
intrasubject COV std [%] 0.3 0.6 0.6
intersubject mean COV [%] 4.2 6.2 10.3

Body#

stats_cc1 = Stats(dataset)
stats_cc1.build_df('body')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
FA (DWI) MD (DWI) RD (DWI)
intrasubject COV mean [%] 0.6 0.7 0.7
intrasubject COV std [%] 0.2 0.2 0.3
intersubject mean COV [%] 3.8 3.0 6.2

Splenium#

stats_cc1 = Stats(dataset)
stats_cc1.build_df('splenium')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
FA (DWI) MD (DWI) RD (DWI)
intrasubject COV mean [%] 0.6 0.7 0.8
intrasubject COV std [%] 0.1 0.2 0.3
intersubject mean COV [%] 2.6 3.1 6.3